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sac-PandaPickAndPlaceDense-v3

Reinforcement Learning
stable-baselines3
PandaPickAndPlaceDense-v3
deep-reinforcement-learning
Eval Results
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Commit sac-100k-v2 model

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  1. README.md +37 -0
  2. config.json +1 -0
  3. replay.mp4 +0 -0
  4. results.json +1 -0
  5. sac-100k-v2-PandaPickAndPlaceDense-v3.zip +3 -0
  6. sac-100k-v2-PandaPickAndPlaceDense-v3/_stable_baselines3_version +1 -0
  7. sac-100k-v2-PandaPickAndPlaceDense-v3/actor.optimizer.pth +3 -0
  8. sac-100k-v2-PandaPickAndPlaceDense-v3/critic.optimizer.pth +3 -0
  9. sac-100k-v2-PandaPickAndPlaceDense-v3/data +126 -0
  10. sac-100k-v2-PandaPickAndPlaceDense-v3/ent_coef_optimizer.pth +3 -0
  11. sac-100k-v2-PandaPickAndPlaceDense-v3/policy.pth +3 -0
  12. sac-100k-v2-PandaPickAndPlaceDense-v3/pytorch_variables.pth +3 -0
  13. sac-100k-v2-PandaPickAndPlaceDense-v3/system_info.txt +8 -0
  14. vec_normalize.pkl +3 -0
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: stable-baselines3
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+ tags:
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+ - PandaPickAndPlaceDense-v3
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+ - deep-reinforcement-learning
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+ - reinforcement-learning
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+ - stable-baselines3
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+ model-index:
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+ - name: sac
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+ results:
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+ - task:
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+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
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+ name: PandaPickAndPlaceDense-v3
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+ type: PandaPickAndPlaceDense-v3
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+ metrics:
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+ - type: mean_reward
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+ value: -10.52 +/- 3.95
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+ name: mean_reward
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+ verified: false
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+ ---
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+
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+ # **sac** Agent playing **PandaPickAndPlaceDense-v3**
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+ This is a trained model of a **sac** agent playing **PandaPickAndPlaceDense-v3**
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+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
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+
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+ ## Usage (with Stable-baselines3)
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+ TODO: Add your code
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+
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+
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+ ```python
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+ from stable_baselines3 import ...
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+ from huggingface_sb3 import load_from_hub
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+
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+ ...
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+ ```
config.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "stable_baselines3.sac.policies", "__doc__": "\n Policy class (with both actor and critic) for SAC.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function MultiInputPolicy.__init__ at 0x7fadee7c1480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fadee7c9240>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, 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